Epidemiology of caprine brucellosis in family farms in the south east of Algeria.

This cross-sectional study aimed to estimate the seroprevalence and the potential risk factors of Brucella infection among goats in family farms in the southern east of Algeria. A total of 196 sera samples were randomly collected from 59 family farms and tested in parallel by Rose Bengal test (RBT) and indirect ELISA (iELISA). A structured questionnaire was used to collect information on potential risk factors.

Extrinsic and intrinsic drivers of prevalence and abundance of hard-bodied ticks (Acari: Ixodidae) in one-humped camel ().

Background: Ticks are ectoparasites and can be vectors of a wide range of pathogens, posing significant health risks to livestock. In the Sahara Desert of Algeria, particularly among one-humped camels (), there is a need to better understand the factors influencing tick infestation patterns to improve livestock management and health outcomes.

Objectives: This study aimed to investigate the prevalence, intensity, and abundance of hard-bodied ticks (Acari: Ixodidae) among dromedaries, examining both intrinsic factors (sex, age, coat color) and extrinsic variables (farming systems, vegetation types, climate zones, and elevation) that might influence tick infestation in this region.

Ultrasonic chemical synthesis of zinc-manganese ferrites with improved magnetic properties.

Zinc-Manganese spinel ferrites (ZnMnFeO) are nowadays very attractive magnetic materials for cancer diagnostic and therapy. With the help of intense ultrasonic waves, sonochemical synthesis method was used to prepare stoichiometric and chemically homogenous nanoparticles by varying the manganese content. The crystal structure along with the size and shape of the as-prepared nanoparticles were described using XRD, TEM and FT-IR techniques, while cations distribution was carefully investigated using XPS and Mössbauer spectroscopic techniques and supported with density functional theory calculations.

Physical Activity Integration in Blood Glucose Level Prediction: Different Levels of Data Fusion.

Blood glucose level (BGL) prediction contributes to more effective management of type 1 diabetes. Physical activity (PA) is a crucial factor in diabetes management. It affects BGL, and it is imperative to effectively deploy PA in BGL prediction to support diabetes management systems by incorporating this crucial factor.

Data-driven blood glucose level prediction in type 1 diabetes: a comprehensive comparative analysis.

Accurate prediction of blood glucose level (BGL) has proven to be an effective way to help in type 1 diabetes management. The choice of input, along with the fundamental choice of model structure, is an existing challenge in BGL prediction. Investigating the performance of different data-driven time series forecasting approaches with different inputs for BGL prediction is beneficial in advancing BGL prediction performance.

In Vitro Glucose Measurement from NIR and MIR Spectroscopy: Comprehensive Benchmark of Machine Learning and Filtering Chemometrics.

The quantitative analysis of glucose using spectroscopy is a topic of great significance and interest in science and industry. One conundrum in this area is deploying appropriate preprocessing and regression tools. To contribute to addressing this challenge, in this study, we conducted a comprehensive and novel comparative analysis of various machine learning and preprocessing filtering techniques applied to near-infrared, mid-infrared, and a combination of near-infrared and mid-infrared spectroscopy for glucose assay.

Preferred surface orientation for CO oxidation on SnO surfaces.

In the present study, we perform a comparative study on the oxidation mechanism of CO gas molecules on SnO (110), (101), and (100) surfaces. The optimized adsorption configurations show that the adsorption of CO molecules could occur similarly on the three SnO surfaces two adsorption modes, physisorption of CO on the Sn site that is considered as the first step for CO oxidation, followed by CO chemisorption on the O site resulting in the formation of CO species. Based on the calculated adsorption energies and CO molecule diffusion on SnO surfaces, CO molecule adsorption on the (101) surface exhibits the highest adsorption energy and the lowest reaction barrier for CO oxidation compared to the widely considered (110) surface or the (100) surface.

Serological evidence of Rift Valley fever viral infection among camels imported into Southern Algeria.

Rift Valley fever (RVF) is a mosquito-borne viral zoonosis caused by the Rift Valley fever virus (RVFV). The present work aims to investigate the epidemiological status and identify the risk factors associated with RVFV infection in dromedary camels (Camelus dromedarius) from southern Algeria. A total of 269 sera of apparently healthy camels was collected and tested using a competitive Enzyme-Linked Immunosorbent Assay (ELISA).

Corrigendum: Automatic inference of hypoglycemia causes in type 1 diabetes: a feasibility study.

[This corrects the article DOI: 10.3389/fcdhc.2023.

Automatic inference of hypoglycemia causes in type 1 diabetes: a feasibility study.

Background: Hypoglycemia is the most common adverse consequence of treating diabetes, and is often due to suboptimal patient self-care. Behavioral interventions by health professionals and self-care education helps avoid recurrent hypoglycemic episodes by targeting problematic patient behaviors. This relies on time-consuming investigation of reasons behind the observed episodes, which involves manual interpretation of personal diabetes diaries and communication with patients.

Blood Glucose Level Time Series Forecasting: Nested Deep Ensemble Learning Lag Fusion.

Blood glucose level prediction is a critical aspect of diabetes management. It enables individuals to make informed decisions about their insulin dosing, diet, and physical activity. This, in turn, improves their quality of life and reduces the risk of chronic and acute complications.

Ethnobotanical survey of the traditional antiparasitic use of medicinal plants in humans and animals in Laghouat (Southern Algeria).

Background And Aim: An ethnobotanical survey was carried out among the inhabitants of the Aflou region of Laghouat (Southern Algeria). This study was considered as a first step toward the identification of new bioactive antiparasitic molecules. The preservation and documentation of this traditional knowledge will ensure its continuity and transmission from one generation to another, especially because of the emergence of resistant parasites and the lack of references caused by the lack of work in this area; therefore, we intended to inventory and collect the maximum amount of information on medicinal plants that are traditionally used by the local population as antiparasitic in humans and animals (small ruminants, cattle, and livestock).

Causality analysis in type 1 diabetes mellitus with application to blood glucose level prediction.

Effective control of blood glucose level (BGL) is the key factor in the management of type 1 diabetes mellitus (T1D). BGL prediction is an important tool to help maximise the time BGL is in the target range and thus minimise both acute and chronic diabetes-related complications. To predict future BGL, histories of variables known to affect BGL, such as carbohydrate intake, injected bolus insulin, and physical activity, are utilised.

Follicular fluid and serum biochemical and hormonal profiles of normal and cystic dromedary camel breeds.

Background And Aim: Ovarian cysts (OC) in female dromedary camels have been described as problematic because they can cause infertility. This study aimed to compare the hormone concentrations and biochemical contents present in serum and follicular fluid of normal and cystic she-dromedaries of the two most common Algerian camel breeds (Sahraoui and Targui) to gain a better understanding of biological differences that may yield insights into preventing or treating this ovarian abnormality.

Materials And Methods: At an abattoir in southeastern Algeria, 100 pairs of the same females' ovaries and blood samples were taken immediately after the slaughter of clinically healthy, non-pregnant females (8-15 years old) over two consecutive breeding seasons (November 2017-April 2018 and November 2018-April 2019).

Interpretable Machine Learning for Inpatient COVID-19 Mortality Risk Assessments: Diabetes Mellitus Exclusive Interplay.

People with diabetes mellitus (DM) are at elevated risk of in-hospital mortality from coronavirus disease-2019 (COVID-19). This vulnerability has spurred efforts to pinpoint distinctive characteristics of COVID-19 patients with DM. In this context, the present article develops ML models equipped with interpretation modules for inpatient mortality risk assessments of COVID-19 patients with DM.

Analysis of real-world capillary blood glucose data to help reduce HbA and hypoglycaemia in type 1 diabetes: Evidence in favour of using the percentage of readings in target and coefficient of variation.

Aims: To examine real-world capillary blood glucose (CBG) data according to HbA to define proportions of CBG readings at different HbA levels, and evaluate patterns in CBG measurements to suggest areas to focus on with regard to self-management.

Methods: A retrospective analysis stratified 682 adults with type 1 diabetes split into quartiles based on their HbA . The proportions of results in different CBG ranges and associations with HbA were evaluated.

Signal fragmentation based feature vector generation in a model agnostic framework with application to glucose quantification using absorption spectroscopy.

This paper proposes feature vector generation based on signal fragmentation equipped with a model interpretation module to enhance glucose quantification from absorption spectroscopy signals. For this purpose, near-infrared (NIR) and mid-infrared (MIR) spectra collected from experimental samples of varying glucose concentrations are scrutinised. Initially, a given spectrum is optimally dissected into several fragments.

COVID-19 mortality risk assessments for individuals with and without diabetes mellitus: Machine learning models integrated with interpretation framework.

This research develops machine learning models equipped with interpretation modules for mortality risk prediction and stratification in cohorts of hospitalised coronavirus disease-2019 (COVID-19) patients with and without diabetes mellitus (DM). To this end, routinely collected clinical data from 156 COVID-19 patients with DM and 349 COVID-19 patients without DM were scrutinised. First, a random forest classifier forecasted in-hospital COVID-19 fatality utilising admission data for each cohort.

Blood Glucose Level Prediction: Advanced Deep-Ensemble Learning Approach.

Optimal and sustainable control of blood glucose levels (BGLs) is the aim of type-1 diabetes management. The automated prediction of BGL using machine learning (ML) algorithms is considered as a promising tool that can support this aim. In this context, this paper proposes new advanced ML architectures to predict BGL leveraging deep learning and ensemble learning.

Sammon's mapping regression for the quantitative analysis of glucose from both mid infrared and near infrared spectra.

This paper proposes a novel regression method based on Sammon's mapping dimensionality reduction technique for the quantification of glucose from both near infrared and mid infrared spectra. The proposed regression model was validated to determine the concentration of glucose from the spectra of aqueous mixtures consisting of human serum albumin and glucose in phosphate buffer solution from both near infrared (NIR) and mid infrared (MIR) regions. The performance of the proposed prediction model has been analysed with traditional regression methods principal component regression (PCR) and partial least squares regression (PLSR) models.

Ethnobotanical and ethnomedicinal analysis of wild medicinal plants traditionally used in Naâma, southwest Algeria.

Algerian people largely rely on traditional medicine practices as part of a community's identity. This first ethnobotanical study aimed to quantify and document the wild medicinal plant taxa from four family and the related traditional knowledge in Naâma province, Algeria. The survey was carried out between 2018 and 2020.

Protocol for a cluster randomised controlled trial of the DAFNE (Dose Adjustment For Normal Eating) intervention compared with 5x1 DAFNE: a lifelong approach to promote effective self-management in adults with type 1 diabetes.

Introduction: The successful treatment of type 1 diabetes (T1D) requires those affected to employ insulin therapy to maintain their blood glucose levels as close to normal to avoid complications in the long-term. The Dose Adjustment For Normal Eating (DAFNE) intervention is a group education course designed to help adults with T1D develop and sustain the complex self-management skills needed to adjust insulin in everyday life. It leads to improved glucose levels in the short term (manifest by falls in glycated haemoglobin, HbA1c), reduced rates of hypoglycaemia and sustained improvements in quality of life but overall glucose levels remain well above national targets.

New Haplotypes of Trypanosoma evansi Identified in Dromedary Camels from Algeria.

Purpose: Surra is a zoonotic disease caused by Trypanosoma evansi (Trypanozoon), a salivary trypanosome native to Africa which affects a wide range of mammals worldwide and causes mortality and significant economic loss. The present study was devoted to the molecular characterization of T. evansi derived from naturally infected dromedary camels in Algeria.